基于统计显著性检验的高效用项集挖掘算法OA北大核心CSTPCD

Aiming at the problem of traditional high utility itemset mining algorithms reporting false positive high utility item-sets in transactions with class labels,this paper proposed two high utility itemset mining algorithms called FHUI and PHUI.The FHUI and PHUI firstly found all the candidates and grouped them by length.Then,the FHUI established null distribu-tions with the frequency distributions,while the PHUI established null distributions by the permutation strategy within or be-tween transactions.Finally,the FHUI and PHUI calculated the p values from the null distributions and exploited the false dis-covery rate to eliminate the false positive high utility itemsets.The experiments on the benchmark data sets show that the FHUI and PHUI can eliminate a large number of false positive itemsets,which allows them to achieve higher accuracy rates in the classification tasks.The experiments on synthetic data sets reveal that the proportions of false positive itemsets reported by FHUI and PHUI are lower than 4.8％and the average utility values are higher than 39 000.Experimental results prove that the statistically significant high utility itemsets reported by the FHUI and PHUI are more reliable and practical in transactions with class labels.